
import io
import os
import pywt
import glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.signal as sig
from scipy import signal
from pywt import wavedec
from scipy.io import loadmat
from scipy.stats import entropy
from scipy.signal import filtfilt
from scipy.signal import butter, lfilter, welch
from sklearn import datasets, linear_model, metrics


# Mengakses seluruh csv file pada drive
raw_data = pd.read_csv('data eeg 1.csv', header = None)

# Membuat dataframe
df_raw = pd.DataFrame(raw_data)
df_raw


# Set the sampling frequency in Hz
fs = 128

# Define the time ranges to crop
start_time = 20
end_time = 30
start_time1 = 40
end_time1 = 50

# Convert the time ranges to index ranges
start_idx = int(start_time * fs)
end_idx = int(end_time * fs)
start_idx1 = int(start_time1 * fs)
end_idx1 = int(end_time1 * fs)

# Crop the data to the first time range
hold_hand = df_raw.loc[start_idx:end_idx]

# Crop the data to the second time range
open_hand = df_raw.loc[start_idx1:end_idx1]

hold_hand
hold_hand.to_csv('Hold Hand Raw Data.csv')


open_hand
open_hand.to_csv('Open Hand Raw Data.csv')


hold_hand


open_hand


hold_f3 = pd.read_csv('hold hand f3.csv', header = None)
hold_f3


# Original Hold Hand F3
for column in hold_f3.columns:  # Loop melalui kolom-kolom DataFrame
  plt.figure()
  plt.title(f"Column-{column}")  # Display the custom title
  plt.plot(hold_f3[column])
  plt.show()


open_f3 = pd.read_csv('open hand F3.csv', header = None)
open_f3


# Original Open F3
for column in open_f3.columns:  # Loop melalui kolom-kolom DataFrame
  plt.figure()
  plt.title(f"Column-{column}")  # Display the custom title
  plt.plot(open_f3[column])
  plt.show()


# Filtering
fs = 128
highcut = 30
lowcut = 0.5

def butter_bandpass(lowcut, highcut, fs, order=4):
    nyq = 0.5 * fs
    low = lowcut / nyq
    high = highcut / nyq
    filter_coeffs = signal.firwin(order, [low, high], pass_zero=False)
    b = filter_coeffs
    a = 1
    return b, a

def apply_bandpass_filter(hold_f3, lowcut, highcut, fs):
    b, a = butter_bandpass(lowcut, highcut, fs)
    filtered_data = np.zeros_like(hold_f3)
    for i, column in enumerate(hold_f3.columns):
        data = hold_f3[column].values
        filtered_data[:, i] = signal.filtfilt(b, a, data)
    return pd.DataFrame(filtered_data, columns=hold_f3.columns)

# Apply the bandpass filter to all columns in the hold_f3 dataframe
hold_hand_filtered = apply_bandpass_filter(hold_f3, lowcut, highcut, fs)
filtered_hold_data = pd.DataFrame(hold_hand_filtered)
filtered_hold_data


# Filtering Hold F3
for column in hold_hand_filtered.columns:  # Loop melalui kolom-kolom DataFrame
  plt.figure()
  plt.title(f"Column-{column}")  # Display the custom title
  plt.plot(hold_hand_filtered[column])
  plt.show()


# Filtering Open F3
def apply_bandpass_filter(open_f3, lowcut, highcut, fs):
    b, a = butter_bandpass(lowcut, highcut, fs)
    filtered_data = np.zeros_like(open_f3)
    for i, column in enumerate(open_f3.columns):
        data = open_f3[column].values
        filtered_data[:, i] = signal.filtfilt(b, a, data)
    return pd.DataFrame(filtered_data, columns=open_f3.columns)

# Apply the bandpass filter to all columns in the open_f3 dataframe
open_hand_filtered = apply_bandpass_filter(open_f3, lowcut, highcut, fs)
filtered_open_data = pd.DataFrame(open_hand_filtered)
filtered_open_data


# Filtering Open F3
for column in open_hand_filtered.columns:  # Loop melalui kolom-kolom DataFrame
  plt.figure()
  plt.title(f"Column-{column}")  # Display the custom title
  plt.plot(open_hand_filtered[column])
  plt.show()


# Compute FFT for each column in hold_f3_filtered dataframe
fft_data = np.fft.fft(hold_hand_filtered.values, axis=0)
fft_freq = np.fft.fftfreq(hold_hand_filtered.shape[0], 1/fs)
fft_hold_df = pd.DataFrame(columns=hold_hand_filtered.columns, index=fft_freq[:hold_hand_filtered.shape[0]//2])

# Plot FFT for each column
for i, column in enumerate(hold_hand_filtered.columns):
  fig, ax=plt.subplots(figsize=(8,6))
  fft_hold_df[column] = np.abs(fft_data[:hold_hand_filtered.shape[0]//2, i])
  ax.plot(fft_hold_df[column])
  ax.set_xlabel('Frequency (Hz)')
  ax.set_ylabel('Magnitude')
  ax.set_xlim([0,65])
  ax.set_ylim([0,50000])
  plt.show()

fft_hold_df
fft_hold_indexing = fft_hold_df.reset_index(drop = True)
fft_hold_indexing


# Compute FFT for each column in open_f3_filtered dataframe
fft_data = np.fft.fft(open_hand_filtered.values, axis=0)
fft_freq = np.fft.fftfreq(open_hand_filtered.shape[0], 1/fs)
fft_open_df = pd.DataFrame(columns=open_hand_filtered.columns, index=fft_freq[:open_hand_filtered.shape[0]//2])

# Plot FFT for each column
for i, column in enumerate(open_hand_filtered.columns):
  fig, ax=plt.subplots(figsize=(8,6))
  fft_open_df[column] = np.abs(fft_data[:open_hand_filtered.shape[0]//2, i])
  ax.plot(fft_open_df[column])
  ax.set_xlabel('Frequency (Hz)')
  ax.set_ylabel('Magnitude')
  ax.set_xlim([0,65])
  ax.set_ylim([0,50000])
  plt.show()

fft_open_df
fft_open_indexing = fft_open_df.reset_index(drop = True)
fft_open_indexing


import os
import glob
import pandas as pd
import numpy as np
from scipy.signal import butter, lfilter, welch
from pywt import wavedec

def extract_band_power(fft_hold_indexing, fs, band, level):
  # Create an empty dictionary to hold the band powers for each column
  band_powers = {}
    
  # Loop over all columns in the DataFrame
  for column in fft_hold_indexing.columns:
    # Apply wavelet decomposition with specified level
    coeffs = wavedec(fft_hold_indexing[column], 'db4', level=level)

    # Get the approximate coefficients of the specified level
    cA = coeffs[level]

    # Compute power spectral density (PSD) using Welch's method
    f, Pxx = welch(cA, fs, nperseg=min(256, len(cA)))

    # Define frequency bands
    freq_bands = {'delta': [0.5, 4],
                  'theta': [4, 8],
                  'alpha': [8, 13],
                  'beta': [13, 30]}

    # Define frequency indices
    freq_ix = {'delta': np.logical_and(f >= freq_bands['delta'][0], f < freq_bands['delta'][1]),
               'theta': np.logical_and(f >= freq_bands['theta'][0], f < freq_bands['theta'][1]),
               'alpha': np.logical_and(f >= freq_bands['alpha'][0], f < freq_bands['alpha'][1]),
               'beta': np.logical_and(f >= freq_bands['beta'][0], f < freq_bands['beta'][1])}

    # Calculate power in the specified band
    mask = freq_ix[band]
    band_power = np.trapz(y=Pxx[mask], x=f[mask])

    # Add the band power to the dictionary for this column
    band_powers[column] = band_power
    
  # Create a DataFrame from the band powers dictionary and return it
  return pd.DataFrame.from_dict(band_powers, orient='index',columns=[band])

# Define the sampling frequency and wavelet decomposition level
fs = 128
level = 4

# Define the input DataFrame with 20 columns of hold hand F3 data
# Replace this with your own DataFrame
#fft_hold_indexing = pd.DataFrame(np.random.randn(1000, 20))

# Loop over all frequency bands and compute the band powers for each column
band_power_hold = pd.DataFrame()
for band in ['delta', 'theta', 'alpha', 'beta']:
    band_power = extract_band_power(fft_hold_indexing, fs, band, level)
    band_power_hold= pd.concat([band_power_hold, band_power], axis=1)

band_power_hold


# Power Spectral Density Open
def extract_band_power(fft_open_indexing, fs, band, level):
    # Create an empty dictionary to open the band powers for each column
    band_powers = {}
    
    # Loop over all columns in the DataFrame
    for column in fft_open_indexing.columns:
        # Apply wavelet decomposition with specified level
        coeffs = wavedec(fft_open_indexing[column], 'db4', level=level)

        # Get the approximate coefficients of the specified level
        cA = coeffs[level]

        # Compute power spectral density (PSD) using Welch's method
        f, Pxx = welch(cA, fs, nperseg=min(256, len(cA)))

        # Define frequency bands
        freq_bands = {'delta': [0.5, 4],
                      'theta': [4, 8],
                      'alpha': [8, 13],
                      'beta': [13, 30]}

        # Define frequency indices
        freq_ix = {'delta': np.logical_and(f >= freq_bands['delta'][0], f < freq_bands['delta'][1]),
                   'theta': np.logical_and(f >= freq_bands['theta'][0], f < freq_bands['theta'][1]),
                   'alpha': np.logical_and(f >= freq_bands['alpha'][0], f < freq_bands['alpha'][1]),
                   'beta': np.logical_and(f >= freq_bands['beta'][0], f < freq_bands['beta'][1])}

        # Calculate power in the specified band
        mask = freq_ix[band]
        band_power = np.trapz(y=Pxx[mask], x=f[mask])

        # Add the band power to the dictionary for this column
        band_powers[column] = band_power
    
    # Create a DataFrame from the band powers dictionary and return it
    return pd.DataFrame.from_dict(band_powers, orient='index', columns=[band])

# Define the sampling frequency and wavelet decomposition level
fs = 128
level = 4

# Define the input DataFrame with 20 columns of open hand F3 data
# Replace this with your own DataFrame
fft_open_indexing = pd.DataFrame(np.random.randn(1000, 20))

# Loop over all frequency bands and compute the band powers for each column
band_power_open = pd.DataFrame()
for band in ['delta', 'theta', 'alpha', 'beta']:
    band_power = extract_band_power(fft_open_indexing, fs, band, level)
    band_power_open= pd.concat([band_power_open, band_power], axis=1)


from scipy.stats import entropy
entropy_hold = np.apply_along_axis(entropy,1,abs(band_power_hold))
entropy_hold_df = pd.DataFrame(entropy_hold, columns=['entropy'])

entropy_hold_df


from scipy.stats import entropy
entropy_open = np.apply_along_axis(entropy,1,abs(band_power_open))
entropy_open_df = pd.DataFrame(entropy_open, columns=['entropy'])

entropy_open_df


#Menjadikan satu dataframe
df_entropy = pd.concat([entropy_hold_df, entropy_open_df])
df_entropy


#Menjadikan satu dataframe
df_combi=pd.concat([band_power_hold, band_power_open])
df_combi


#Mencari mean
mean = df_combi.mean(axis=1)
df_mean = pd.DataFrame(mean, columns=['mean'])
df_mean


#Mencari variance
var = df_combi.var(axis=1)
df_var = pd.DataFrame(var, columns=['var'])
df_var


#Mencari Standar Deviasi
std = df_combi.std(axis=1)
df_std = pd.DataFrame(std, columns=['std'])
df_std


#Membuat dataframe baru dari feature yang baru
df_new_feat = pd.concat([df_entropy, df_mean, df_var, df_std], axis=1)
df_new_feat


# Targeting
df_new_feat['target'] = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                        1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
df_new_feat


import seaborn as sns

# Compute the correlation matrix
correlation_matrix = df_new_feat.corr()

# Create a heatmap with labels
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')

# Add labels to the x and y axis ticks
plt.xticks(range(len(correlation_matrix.columns)), correlation_matrix.columns)
plt.yticks(range(len(correlation_matrix.columns)), correlation_matrix.columns)

# Display the plot
plt.show()


# Splitting Data into features and target
x=df_new_feat.loc[:,'mean':'var'].to_numpy() #features
y=df_new_feat.loc[:,'target'].to_numpy() #target


import collections
counter = collections.Counter(y)
for label, _ in counter.items():
    row_ix = np.where(y == label)[0]
    plt.scatter(x[row_ix, 0], x[row_ix, 1], label=label)
plt.legend()
plt.show()


from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)


# Training Model - Melakukan training model dengan menggunakan k = 1
from sklearn.neighbors import KNeighborsClassifier
k1 = 1
model_knn_1 = KNeighborsClassifier(n_neighbors = k1)
model_knn_1.fit(x_train, y_train)

KNeighborsClassifier(n_neighbors=1)


# Predicting
y_pred_1 = model_knn_1.predict(x_test)
print(y_pred_1)

[1 1 0 0 0 1 0 0 0 1 0 1]

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Perhitungan Akurasi k = 1
from sklearn import metrics
print("Train set Accuracy : ", metrics.accuracy_score(y_train, model_knn_1.predict(x_train)))
print("Test set Accuracy : ", metrics.accuracy_score(y_test, y_pred_1))

Train set Accuracy :  1.0
Test set Accuracy :  1.0

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Training Model - Melakukan training model dengan menggunakan k = 2
from sklearn.neighbors import KNeighborsClassifier
k2 = 2
model_knn_2 = KNeighborsClassifier(n_neighbors = k2)
model_knn_2.fit(x_train, y_train)

KNeighborsClassifier(n_neighbors=2)


# Predicting
y_pred_2 = model_knn_2.predict(x_test)
print(y_pred_2)

[1 1 0 0 0 1 0 0 0 1 0 1]

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Perhitungan Akurasi K = 2
from sklearn import metrics
print("Train set Accuracy : ", metrics.accuracy_score(y_train, model_knn_2.predict(x_train)))
print("Test set Accuracy : ", metrics.accuracy_score(y_test, y_pred_2))

Train set Accuracy :  1.0
Test set Accuracy :  1.0

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Training Model - Melakukan training model dengan menggunakan k = 3
from sklearn.neighbors import KNeighborsClassifier
k3 = 3
model_knn_3 = KNeighborsClassifier(n_neighbors = k3)
model_knn_3.fit(x_train, y_train)

KNeighborsClassifier(n_neighbors=3)


# Predicting
y_pred_3 = model_knn_3.predict(x_test)
print(y_pred_3)

[1 1 0 0 0 1 0 0 0 1 0 1]

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Perhitungan Akurasi K = 3
from sklearn import metrics
print("Train set Accuracy : ", metrics.accuracy_score(y_train, model_knn_3.predict(x_train)))
print("Test set Accuracy : ", metrics.accuracy_score(y_test, y_pred_3))

Train set Accuracy :  1.0
Test set Accuracy :  1.0

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


model_eval = pd.DataFrame(columns=['Train Accuracy', 'Test Accuracy'])

# Train Set Accuracy
model_eval.loc['K = 1','Train Accuracy'] = metrics.accuracy_score(y_train, model_knn_1.predict(x_train))
model_eval.loc['K = 2','Train Accuracy'] = metrics.accuracy_score(y_train, model_knn_2.predict(x_train))
model_eval.loc['K = 3','Train Accuracy'] = metrics.accuracy_score(y_train, model_knn_3.predict(x_train))
model_eval 

# Test Set Accuracy 
model_eval.loc['K = 1','Test Accuracy'] = metrics.accuracy_score(y_test, y_pred_1)
model_eval.loc['K = 2','Test Accuracy'] = metrics.accuracy_score(y_test, y_pred_2)
model_eval.loc['K = 3','Test Accuracy'] = metrics.accuracy_score(y_test, y_pred_3)
model_eval

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Retraining Menggunakan Nilai K dengan Akurasi Terbaik - K = 1
k = 1
model_knn = KNeighborsClassifier(n_neighbors = k)
model_knn.fit(x_train, y_train)

KNeighborsClassifier(n_neighbors=1)


# Predicting
y_pred = model_knn.predict(x_test)
print(y_pred)

[1 1 0 0 0 1 0 0 0 1 0 1]

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Perhitungan Akurasi k = 1

print("Train set Accuracy : ", metrics.accuracy_score(y_train, model_knn.predict(x_train)))
print("Test set Accuracy : ", metrics.accuracy_score(y_test, y_pred))
print()
print(f"Achieved accuracy of {metrics.accuracy_score(y_test, y_pred)*100}% on test data")

Train set Accuracy :  1.0
Test set Accuracy :  1.0

Achieved accuracy of 100.0% on test data

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Confusion Matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
import seaborn as sns
target_names = ['hold hand', 'open hand']
print(classification_report(y_test, y_pred, target_names=target_names))

f, ax = plt.subplots(figsize=(8,5))
cm = confusion_matrix(y_test, y_pred)
cm_labels = ['hold hand', 'open hand']
sns.heatmap(cm,cmap= "Blues", linecolor = 'black' , linewidth = 1 , annot = True, fmt='', 
            yticklabels = cm_labels,
            xticklabels = cm_labels, ax=ax)
plt.xlabel("Actual Values")
plt.ylabel("Predicted Values")

              precision    recall  f1-score   support

   hold hand       1.00      1.00      1.00         7
   open hand       1.00      1.00      1.00         5

    accuracy                           1.00        12
   macro avg       1.00      1.00      1.00        12
weighted avg       1.00      1.00      1.00        12

Text(70.72222222222221, 0.5, 'Predicted Values')

	0	1	2	3	4	5	6	7	8	9	...	270	271	272	273	274	275	276	277	278	279
0	4498.974121	4498.974121	4498.974121	4498.974121	4498.974121	4498.974121	4498.974121	4498.974121	4498.974121	4498.974121	...	4498.974121	4498.974121	4498.974121	4498.974121	4498.974121	4498.974121	4498.974121	4498.974121	4498.974121	4498.974121
1	4501.538574	4501.538574	4501.538574	4501.538574	4501.538574	4501.538574	4501.538574	4501.538574	4501.538574	4501.538574	...	4501.538574	4501.538574	4501.538574	4501.538574	4501.538574	4501.538574	4501.538574	4501.538574	4501.538574	4501.538574
2	4502.051270	4502.051270	4502.051270	4502.051270	4502.051270	4502.051270	4502.051270	4502.051270	4502.051270	4502.051270	...	4502.051270	4502.051270	4502.051270	4502.051270	4502.051270	4502.051270	4502.051270	4502.051270	4502.051270	4502.051270
3	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	...	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539
4	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	...	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6907	4551.794922	4551.794922	4551.794922	4551.794922	4551.794922	4551.794922	4551.794922	4551.794922	4551.794922	4551.794922	...	4551.794922	4551.794922	4551.794922	4551.794922	4551.794922	4551.794922	4551.794922	4551.794922	4551.794922	4551.794922
6908	4552.820313	4552.820313	4552.820313	4552.820313	4552.820313	4552.820313	4552.820313	4552.820313	4552.820313	4552.820313	...	4552.820313	4552.820313	4552.820313	4552.820313	4552.820313	4552.820313	4552.820313	4552.820313	4552.820313	4552.820313
6909	4552.307617	4552.307617	4552.307617	4552.307617	4552.307617	4552.307617	4552.307617	4552.307617	4552.307617	4552.307617	...	4552.307617	4552.307617	4552.307617	4552.307617	4552.307617	4552.307617	4552.307617	4552.307617	4552.307617	4552.307617
6910	4547.179199	4547.179199	4547.179199	4547.179199	4547.179199	4547.179199	4547.179199	4547.179199	4547.179199	4547.179199	...	4547.179199	4547.179199	4547.179199	4547.179199	4547.179199	4547.179199	4547.179199	4547.179199	4547.179199	4547.179199
6911	4546.153809	4546.153809	4546.153809	4546.153809	4546.153809	4546.153809	4546.153809	4546.153809	4546.153809	4546.153809	...	4546.153809	4546.153809	4546.153809	4546.153809	4546.153809	4546.153809	4546.153809	4546.153809	4546.153809	4546.153809

	0	1	2	3	4	5	6	7	8	9	...	270	271	272	273	274	275	276	277	278	279
2560	4517.435547	4517.435547	4517.435547	4517.435547	4517.435547	4517.435547	4517.435547	4517.435547	4517.435547	4517.435547	...	4517.435547	4517.435547	4517.435547	4517.435547	4517.435547	4517.435547	4517.435547	4517.435547	4517.435547	4517.435547
2561	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	...	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617
2562	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	...	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617	4512.307617
2563	4515.384277	4515.384277	4515.384277	4515.384277	4515.384277	4515.384277	4515.384277	4515.384277	4515.384277	4515.384277	...	4515.384277	4515.384277	4515.384277	4515.384277	4515.384277	4515.384277	4515.384277	4515.384277	4515.384277	4515.384277
2564	4514.871582	4514.871582	4514.871582	4514.871582	4514.871582	4514.871582	4514.871582	4514.871582	4514.871582	4514.871582	...	4514.871582	4514.871582	4514.871582	4514.871582	4514.871582	4514.871582	4514.871582	4514.871582	4514.871582	4514.871582
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3836	4506.666504	4506.666504	4506.666504	4506.666504	4506.666504	4506.666504	4506.666504	4506.666504	4506.666504	4506.666504	...	4506.666504	4506.666504	4506.666504	4506.666504	4506.666504	4506.666504	4506.666504	4506.666504	4506.666504	4506.666504
3837	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	...	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113
3838	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	...	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809
3839	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	...	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113
3840	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	...	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113	4505.641113

	0	1	2	3	4	5	6	7	8	9	...	270	271	272	273	274	275	276	277	278	279
5120	4528.205078	4528.205078	4528.205078	4528.205078	4528.205078	4528.205078	4528.205078	4528.205078	4528.205078	4528.205078	...	4528.205078	4528.205078	4528.205078	4528.205078	4528.205078	4528.205078	4528.205078	4528.205078	4528.205078	4528.205078
5121	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	...	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930
5122	4524.102539	4524.102539	4524.102539	4524.102539	4524.102539	4524.102539	4524.102539	4524.102539	4524.102539	4524.102539	...	4524.102539	4524.102539	4524.102539	4524.102539	4524.102539	4524.102539	4524.102539	4524.102539	4524.102539	4524.102539
5123	4522.051270	4522.051270	4522.051270	4522.051270	4522.051270	4522.051270	4522.051270	4522.051270	4522.051270	4522.051270	...	4522.051270	4522.051270	4522.051270	4522.051270	4522.051270	4522.051270	4522.051270	4522.051270	4522.051270	4522.051270
5124	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	...	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930	4525.127930
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6396	4502.563965	4502.563965	4502.563965	4502.563965	4502.563965	4502.563965	4502.563965	4502.563965	4502.563965	4502.563965	...	4502.563965	4502.563965	4502.563965	4502.563965	4502.563965	4502.563965	4502.563965	4502.563965	4502.563965	4502.563965
6397	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	...	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809	4506.153809
6398	4503.589844	4503.589844	4503.589844	4503.589844	4503.589844	4503.589844	4503.589844	4503.589844	4503.589844	4503.589844	...	4503.589844	4503.589844	4503.589844	4503.589844	4503.589844	4503.589844	4503.589844	4503.589844	4503.589844	4503.589844
6399	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	...	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539	4504.102539
6400	4505.127930	4505.127930	4505.127930	4505.127930	4505.127930	4505.127930	4505.127930	4505.127930	4505.127930	4505.127930	...	4505.127930	4505.127930	4505.127930	4505.127930	4505.127930	4505.127930	4505.127930	4505.127930	4505.127930	4505.127930

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
0	4174.871582	4261.538574	4502.563965	4336.410156	4192.820313	4254.871582	4383.589844	4309.230469	4295.897461	4400.000000	4440.000000	4333.846191	4362.051270	4100.512695	4347.179199	4194.358887	4411.281738	4253.846191	4369.743652	4327.179199
1	4178.974121	4269.743652	4503.589844	4337.436035	4194.358887	4262.563965	4384.615234	4304.615234	4307.692383	4407.179199	4433.333008	4331.281738	4363.589844	4101.025391	4344.102539	4195.384277	4401.538574	4252.820313	4375.384277	4329.230469
2	4173.846191	4272.820313	4504.615234	4340.000000	4192.307617	4264.615234	4381.538574	4302.563965	4319.487305	4405.641113	4426.666504	4333.333008	4361.538574	4104.102539	4333.333008	4200.512695	4401.025391	4250.769043	4374.871582	4326.666504
3	4163.076660	4274.871582	4498.461426	4344.102539	4192.820313	4264.102539	4376.410156	4302.051270	4318.974121	4404.615234	4430.769043	4336.922852	4357.948730	4112.820313	4330.769043	4196.922852	4413.846191	4258.974121	4366.666504	4326.153809
4	4157.948730	4284.102539	4496.410156	4342.051270	4193.333008	4268.205078	4375.897461	4302.051270	4314.358887	4405.641113	4438.974121	4329.743652	4356.922852	4112.307617	4327.692383	4193.333008	4416.922852	4260.000000	4363.076660	4326.153809
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1276	4413.333008	4369.230469	4408.205078	4411.794922	4110.769043	4343.589844	4393.333008	4400.512695	4315.897461	4330.256348	4331.281738	4441.538574	4234.358887	4214.871582	4289.230469	4372.307617	4321.538574	4330.769043	4171.794922	4160.512695
1277	4418.461426	4372.820313	4374.871582	4415.897461	4117.948730	4344.102539	4398.461426	4399.487305	4321.538574	4316.410156	4326.666504	4445.641113	4236.922852	4211.281738	4288.717773	4374.358887	4321.538574	4331.794922	4177.436035	4162.563965
1278	4415.384277	4380.512695	4365.641113	4420.000000	4110.769043	4342.051270	4396.922852	4402.563965	4326.666504	4306.153809	4322.563965	4454.358887	4241.025391	4188.717773	4288.205078	4373.333008	4318.974121	4340.000000	4181.025391	4167.692383
1279	4413.333008	4378.974121	4369.743652	4425.127930	4107.179199	4346.153809	4393.333008	4404.615234	4334.871582	4298.974121	4327.692383	4459.487305	4237.436035	4144.102539	4284.615234	4371.794922	4315.384277	4346.153809	4187.179199	4176.922852
1280	4410.769043	4374.871582	4362.563965	4430.256348	4117.436035	4350.769043	4391.281738	4400.000000	4336.922852	4265.641113	4330.769043	4447.179199	4241.025391	4118.461426	4283.589844	4371.281738	4322.563965	4351.794922	4191.794922	4175.897461

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
0	4275.897461	4336.922852	4444.615234	4615.384277	4026.666504	4141.025391	4318.461426	4300.512695	4463.589844	4288.205078	4357.435547	4252.820313	4414.871582	4269.230469	4281.538574	4258.461426	4406.666504	4409.743652	4403.076660	4382.563965
1	4280.512695	4306.153809	4444.102539	4612.820313	4015.384521	4143.076660	4319.487305	4302.051270	4463.076660	4253.846191	4364.102539	4254.358887	4413.846191	4271.794922	4284.615234	4275.897461	4410.769043	4409.743652	4407.692383	4382.563965
2	4276.410156	4288.205078	4447.179199	4604.615234	4016.410156	4143.076660	4317.948730	4304.615234	4464.615234	4204.615234	4373.846191	4258.974121	4408.205078	4267.692383	4288.717773	4287.179199	4410.256348	4406.153809	4407.179199	4380.000000
3	4277.948730	4290.256348	4450.256348	4598.974121	4006.666504	4146.153809	4317.436035	4329.230469	4467.692383	4168.205078	4377.948730	4265.127930	4410.256348	4266.153809	4289.230469	4300.000000	4404.615234	4411.281738	4402.563965	4373.846191
4	4277.436035	4293.333008	4447.692383	4602.051270	3978.461426	4148.205078	4324.102539	4348.717773	4466.666504	4164.615234	4369.743652	4267.692383	4417.948730	4265.127930	4291.794922	4312.307617	4405.127930	4420.512695	4400.512695	4366.153809
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1276	4310.769043	4301.538574	4373.333008	4321.025391	4212.307617	4517.435547	4386.153809	4333.846191	4096.922852	4402.051270	4239.487305	4361.538574	4469.743652	4238.974121	4151.794922	4283.589844	4331.794922	4392.820313	4327.692383	4397.435547
1277	4317.948730	4298.974121	4380.000000	4318.974121	4206.153809	4517.948730	4378.974121	4342.051270	4100.000000	4400.000000	4253.846191	4360.000000	4476.922852	4237.948730	4165.127930	4287.692383	4335.384277	4391.281738	4321.538574	4402.051270
1278	4327.692383	4288.205078	4378.974121	4306.666504	4198.974121	4513.333008	4376.410156	4350.256348	4097.948730	4401.538574	4221.538574	4342.051270	4483.589844	4239.487305	4179.487305	4292.307617	4330.769043	4392.307617	4317.948730	4398.461426
1279	4332.820313	4285.641113	4376.922852	4276.922852	4201.025391	4512.820313	4381.025391	4347.692383	4100.512695	4411.794922	4145.641113	4317.948730	4485.127930	4241.025391	4181.538574	4302.051270	4327.692383	4389.743652	4313.333008	4394.358887
1280	4329.743652	4292.820313	4378.461426	4262.051270	4204.615234	4517.948730	4381.538574	4347.692383	4109.743652	4409.743652	4085.640869	4313.333008	4486.666504	4244.615234	4176.922852	4307.692383	4329.230469	4386.666504	4309.230469	4393.333008

Data Acquisition¶

Import Libraries, Function and Files¶

Cropping Data¶

Evaluation¶

Accuracy¶

Confusion Matrix¶

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
0	5024.853147	5129.165076	5419.261950	5219.280112	5046.456144	5121.140720	5276.065333	5186.566786	5170.519249	5295.816509	5343.960295	5216.194138	5250.141620	4935.355188	5232.241675	5048.307963	5309.395149	5119.906565	5259.400130	5208.169782
1	5026.467181	5137.244937	5420.331169	5221.080287	5047.125138	5128.389057	5275.833953	5181.935435	5184.427051	5301.452925	5336.188767	5214.736180	5250.716222	4936.987100	5226.066793	5050.759236	5301.169225	5118.552542	5263.882254	5209.089229
2	5021.583855	5142.435651	5419.338236	5224.026795	5046.641528	5131.955623	5272.898693	5179.148601	5194.555699	5302.626322	5331.735874	5215.966166	5248.969957	4941.472792	5218.221832	5052.791898	5301.655552	5119.561835	5262.872795	5208.170622
3	5012.666237	5147.648533	5415.574870	5226.455939	5046.385644	5133.786248	5269.045162	5178.174833	5196.849075	5301.985747	5334.308338	5216.196163	5246.185222	4946.963096	5212.123332	5051.360416	5309.194484	5123.593130	5257.243399	5207.288542
4	5007.386998	5156.017021	5411.749619	5226.187238	5045.266011	5137.188382	5267.956509	5178.388895	5196.568196	5301.194521	5339.665017	5211.104921	5245.921086	4947.434131	5203.363194	5049.581500	5312.694793	5123.766572	5253.326926	5208.806859
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1276	5311.852946	5258.163781	5303.524583	5307.396895	4950.490585	5226.880276	5288.678842	5295.665328	5196.072703	5206.580330	5211.312931	5344.742873	5098.994616	5073.469073	5162.444041	5262.591513	5203.141336	5215.934386	5021.769226	5006.398145
1277	5315.219860	5264.261122	5274.067715	5314.796259	4951.659450	5227.731990	5291.669712	5296.534971	5201.368726	5196.144538	5207.824334	5352.184244	5100.071201	5061.655606	5161.806391	5263.880532	5200.601774	5216.390474	5027.326025	5011.113541
1278	5314.485442	5269.197363	5259.378843	5320.240324	4948.935819	5228.153567	5291.283662	5298.486225	5208.465749	5183.397368	5205.775994	5359.698593	5102.016175	5033.877038	5160.253994	5263.494944	5198.132454	5223.049560	5033.065705	5017.465169
1279	5311.706783	5269.409278	5255.816440	5326.084401	4948.248795	5231.327144	5288.273916	5299.041894	5215.369732	5165.243216	5208.292499	5361.298353	5102.461681	4993.865014	5157.755980	5262.210457	5197.676060	5230.785525	5039.192958	5023.869143
1280	5308.778072	5265.572079	5250.758697	5332.232888	4955.724029	5236.562392	5285.323255	5295.816509	5219.897190	5134.102870	5212.490499	5352.601141	5104.475518	4956.958184	5155.705867	5261.251362	5202.614911	5237.797134	5045.221989	5026.087889

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
0	5146.447356	5219.897190	5349.515166	5555.051875	4846.474306	4984.116052	5197.677117	5176.074120	5372.352906	5161.260738	5244.586161	5118.671823	5313.715866	5138.423000	5153.236969	5125.461437	5303.840278	5307.543916	5299.519560	5274.830590
1	5149.115231	5187.767444	5350.137929	5550.077006	4836.896229	4985.946154	5198.051362	5178.792116	5372.479392	5115.072334	5253.538502	5121.622772	5311.065784	5139.261885	5157.212422	5144.352804	5307.063950	5306.506262	5303.205389	5273.858834
2	5148.766392	5168.506427	5352.555836	5543.050590	4830.267515	4987.578827	5197.521906	5188.547508	5374.072521	5065.517324	5262.171321	5126.600963	5308.378747	5137.313580	5160.704179	5160.401572	5306.418049	5306.251218	5302.976865	5270.407452
3	5148.216114	5164.122705	5354.471913	5538.378717	4816.461038	4989.956935	5198.891588	5208.789558	5375.907995	5028.679588	5265.063720	5132.337678	5310.123625	5134.774740	5163.062883	5175.179488	5303.310281	5310.741338	5299.740096	5263.848304
4	5146.622184	5160.875719	5354.921359	5539.172088	4798.810868	4992.069981	5203.616251	5225.243175	5376.093093	5016.173603	5261.999560	5137.351577	5314.950075	5130.814814	5164.729663	5188.234741	5301.031726	5316.389664	5297.153908	5256.838997
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1276	5187.068494	5178.087517	5263.893562	5201.048211	5062.473685	5439.082398	5276.455710	5220.354885	4930.160652	5295.824067	5106.241413	5247.965689	5384.728054	5102.138091	5001.113578	5154.678928	5213.713836	5287.339217	5208.297989	5295.060605
1277	5197.733662	5171.635584	5269.080083	5194.376030	5061.871950	5436.224047	5272.093003	5226.104866	4933.014044	5297.081313	5103.029112	5241.768577	5388.438109	5101.671863	5013.456626	5160.867736	5215.243215	5286.138752	5202.218116	5295.598909
1278	5207.054293	5164.141419	5270.059400	5177.546073	5057.064538	5433.659768	5269.920542	5232.278272	4933.903426	5300.483207	5065.203209	5224.006994	5394.641790	5102.731848	5026.016613	5167.890931	5212.940126	5285.361845	5196.775697	5293.670785
1279	5212.013226	5161.734529	5269.256168	5152.418422	5057.078799	5433.652221	5271.728225	5233.495987	4937.767987	5305.957475	4994.137724	5203.654225	5398.156517	5105.186830	5030.283689	5176.625614	5210.416869	5283.211946	5191.659469	5290.076170
1280	5211.256344	5166.815610	5269.892796	5129.782154	5060.652448	5437.778971	5273.596435	5232.859340	4946.465518	5307.543916	4917.455537	5191.504580	5400.127850	5108.796234	5027.322045	5184.715554	5210.638679	5279.768384	5186.566786	5287.792153

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
0	6.636300e+06	6.737951e+06	6.747544e+06	6.768863e+06	6.322417e+06	6.449103e+06	6.629133e+06	6.637294e+06	6.695320e+06	6.673823e+06	6.757264e+06	6.676565e+06	6.688746e+06	6.458250e+06	6.533107e+06	6.683776e+06	6.691407e+06	6.671431e+06	6.756407e+06	6.695311e+06
1	2.890031e+04	3.510311e+04	6.865284e+04	4.249624e+04	1.396632e+04	8.588998e+04	2.257083e+04	4.124650e+04	4.479839e+04	1.416289e+04	2.573191e+04	3.273695e+04	3.337751e+04	8.040925e+03	3.655462e+04	3.773879e+04	6.924294e+04	2.705638e+04	5.410361e+04	3.399721e+04
2	4.883446e+04	3.773153e+04	1.397937e+04	4.773215e+04	8.307530e+03	6.388318e+04	5.159552e+04	3.937091e+04	9.201755e+03	3.715874e+04	1.436479e+04	1.275797e+04	9.106575e+03	4.211172e+04	3.442374e+04	3.983278e+03	2.020839e+04	1.332913e+04	2.665561e+04	1.477590e+04
3	1.958175e+04	8.930867e+04	3.618132e+04	1.497246e+04	2.739165e+03	1.239559e+04	3.754245e+04	3.843926e+04	2.288784e+04	3.462916e+04	1.870987e+04	1.981819e+04	2.418237e+04	1.572301e+04	1.009368e+04	3.465779e+04	4.765370e+04	1.213243e+04	1.843544e+04	3.857719e+04
4	2.065794e+04	1.063432e+04	2.435139e+04	1.762362e+04	1.803777e+04	3.300974e+04	1.976621e+04	2.887306e+04	2.858991e+03	3.040117e+04	1.231980e+04	2.190517e+03	3.384236e+03	1.969148e+04	1.927697e+04	2.524337e+04	7.815398e+03	1.171590e+04	1.371440e+04	2.774433e+04
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
635	1.422410e+02	6.939167e+01	8.586760e+01	5.836838e+01	4.221212e+01	6.127798e+01	4.194410e+00	5.205987e+01	2.898925e+01	8.834635e+01	6.677375e+01	6.422973e+01	7.153493e+01	2.889052e+00	3.973120e+01	1.067004e+02	5.419436e+01	6.033364e+01	1.041212e+02	9.061426e+01
636	1.422363e+02	6.938886e+01	8.586524e+01	5.836579e+01	4.221089e+01	6.127610e+01	4.194050e+00	5.205773e+01	2.898741e+01	8.834282e+01	6.677290e+01	6.422727e+01	7.153240e+01	2.887966e+00	3.972817e+01	1.066974e+02	5.419323e+01	6.033191e+01	1.041170e+02	9.061270e+01
637	1.422323e+02	6.938746e+01	8.586282e+01	5.836500e+01	4.220963e+01	6.127435e+01	4.194628e+00	5.205655e+01	2.898705e+01	8.834089e+01	6.677151e+01	6.422630e+01	7.153072e+01	2.886791e+00	3.972837e+01	1.066951e+02	5.419151e+01	6.033060e+01	1.041150e+02	9.060989e+01
638	1.422300e+02	6.938567e+01	8.586164e+01	5.836418e+01	4.220851e+01	6.127336e+01	4.194052e+00	5.205631e+01	2.898658e+01	8.833891e+01	6.676976e+01	6.422577e+01	7.152983e+01	2.886480e+00	3.972743e+01	1.066929e+02	5.419032e+01	6.032922e+01	1.041128e+02	9.060873e+01
639	1.422282e+02	6.938472e+01	8.586059e+01	5.836338e+01	4.220789e+01	6.127283e+01	4.194010e+00	5.205558e+01	2.898628e+01	8.833786e+01	6.676904e+01	6.422478e+01	7.152888e+01	2.885759e+00	3.972699e+01	1.066918e+02	5.418976e+01	6.032851e+01	1.041116e+02	9.060750e+01

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
0	6.684979e+06	6.673099e+06	6.703452e+06	6.751660e+06	6.353610e+06	6.684377e+06	6.619395e+06	6.641050e+06	6.545692e+06	6.731540e+06	6.715155e+06	6.789265e+06	6.713466e+06	6.550008e+06	6.609654e+06	6.677508e+06	6.595796e+06	6.679990e+06	6.713161e+06	6.717636e+06
1	4.878734e+04	6.132208e+04	2.180570e+04	2.881542e+04	7.886387e+04	5.353854e+04	1.056872e+05	5.396618e+04	1.230759e+05	4.170276e+04	3.439127e+04	1.991079e+04	1.047562e+05	1.055329e+04	3.481544e+04	2.453769e+04	1.919289e+04	2.391329e+04	2.589755e+04	6.109766e+04
2	3.225872e+04	3.066466e+04	8.079470e+03	1.809973e+03	2.907226e+04	3.687041e+04	2.135241e+04	2.846049e+04	1.724688e+04	5.775082e+04	3.497125e+04	5.570761e+04	1.834590e+04	1.975754e+04	8.019926e+04	2.344656e+04	1.563263e+04	2.206997e+04	7.129059e+04	2.983384e+04
3	2.782172e+04	9.780048e+03	4.269451e+04	1.972497e+04	9.202859e+04	4.357535e+04	1.775309e+04	3.898811e+04	1.557223e+04	1.347905e+04	3.346855e+04	3.596177e+04	5.731249e+04	6.785529e+03	3.888029e+04	1.379389e+04	2.508141e+04	1.731525e+04	4.497296e+04	2.436034e+04
4	2.921642e+04	4.577004e+03	2.552968e+04	1.472075e+04	2.891343e+04	4.407405e+04	4.095236e+04	2.339006e+04	4.201416e+04	1.850360e+04	2.501053e+04	3.450731e+04	4.157100e+04	2.132816e+04	2.221830e+04	5.125299e+03	2.554091e+04	8.830027e+03	2.338242e+04	4.294962e+04
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
635	3.286435e+01	3.356942e+01	3.950762e+01	2.183498e+02	1.050151e+02	2.288398e+02	3.845772e+01	2.883875e+01	2.104443e+02	6.213639e+01	1.798990e+02	3.513176e+01	4.326734e+01	1.315644e+01	6.325174e+01	3.652642e+01	4.511769e+01	1.487692e+01	5.645402e+01	6.044702e+00
636	3.286273e+01	3.356681e+01	3.950509e+01	2.183448e+02	1.050135e+02	2.288327e+02	3.845676e+01	2.883866e+01	2.104384e+02	6.213411e+01	1.798931e+02	3.512936e+01	4.326537e+01	1.315679e+01	6.324850e+01	3.652566e+01	4.511587e+01	1.487786e+01	5.645299e+01	6.044311e+00
637	3.286155e+01	3.356612e+01	3.950333e+01	2.183392e+02	1.050103e+02	2.288278e+02	3.845586e+01	2.883746e+01	2.104331e+02	6.213322e+01	1.798895e+02	3.512865e+01	4.326422e+01	1.315627e+01	6.324597e+01	3.652517e+01	4.511479e+01	1.487739e+01	5.645194e+01	6.044423e+00
638	3.286145e+01	3.356534e+01	3.950275e+01	2.183352e+02	1.050084e+02	2.288234e+02	3.845532e+01	2.883642e+01	2.104293e+02	6.213141e+01	1.798864e+02	3.512734e+01	4.326374e+01	1.315575e+01	6.324587e+01	3.652423e+01	4.511380e+01	1.487754e+01	5.645065e+01	6.044645e+00
639	3.286085e+01	3.356496e+01	3.950226e+01	2.183328e+02	1.050073e+02	2.288207e+02	3.845469e+01	2.883615e+01	2.104265e+02	6.213068e+01	1.798843e+02	3.512680e+01	4.326311e+01	1.315571e+01	6.324486e+01	3.652358e+01	4.511350e+01	1.487715e+01	5.645015e+01	6.044383e+00

	delta	theta	alpha	beta
0	6.124768e+06	1945.696281	1446.855646	17629.364112
1	5.515881e+06	9592.777157	9427.102858	57111.807290
2	5.563086e+06	953.674544	1122.317665	14549.016447
3	6.194153e+06	2011.675340	6556.823969	12109.819969
4	5.301752e+06	4348.808762	6987.937497	22389.729220
5	5.328892e+06	222.168260	389.663237	4738.944545
6	6.100671e+06	554.742058	352.991960	1806.313750
7	5.811218e+06	183.140508	483.927714	2628.130946
8	5.824317e+06	3697.174869	8020.169944	13187.350969
9	6.063106e+06	1066.199757	2223.143740	9457.377581
10	6.242759e+06	923.480231	1015.848882	10190.023580
11	5.885814e+06	2097.148490	1973.268022	8970.773430
12	5.770962e+06	272.988525	812.938319	2209.178032
13	5.816691e+06	377.519089	1593.513483	5199.889132
14	5.681653e+06	1188.561845	1092.776436	4428.330337
15	5.830030e+06	713.711767	1220.914679	7166.986705
16	5.500943e+06	4650.557887	1000.997333	15970.038296
17	5.922758e+06	131.908453	473.629187	7892.765396
18	5.878169e+06	217.206429	2230.945457	4264.438177
19	5.918404e+06	1289.495957	2356.107644	5040.661537

	entropy
0	0.024725
1	0.082028
2	0.021683
3	0.025314
4	0.043761
5	0.008360
6	0.004259
7	0.005156
8	0.031685
9	0.016572
10	0.015115
11	0.017576
12	0.005300
13	0.010369
14	0.010166
15	0.012657
16	0.028336
17	0.011235
18	0.009742
19	0.012424

	entropy
0	0.941611
1	0.907168
2	0.999018
3	1.045866
4	1.126228
5	0.735537
6	1.163915
7	1.040080
8	1.121369
9	1.048369
10	1.201272
11	1.016960
12	1.205832
13	1.070448
14	1.001885
15	0.942521
16	0.834205
17	1.097267
18	1.098067
19	1.138723

	mean
0	1.536447e+06
1	1.398003e+06
2	1.394928e+06
3	1.553708e+06
4	1.333870e+06
5	1.333561e+06
6	1.525846e+06
7	1.453628e+06
8	1.462306e+06
9	1.518963e+06
10	1.563722e+06
11	1.474714e+06
12	1.443564e+06
13	1.455966e+06
14	1.422091e+06
15	1.459783e+06
16	1.380641e+06
17	1.482814e+06
18	1.471220e+06
19	1.481773e+06
0	9.735277e-02
1	9.954872e-02
2	1.078740e-01
3	1.187457e-01
4	8.782040e-02
5	9.675292e-02
6	1.033650e-01
7	1.105460e-01
8	1.185517e-01
9	9.398822e-02
10	1.185421e-01
11	1.115141e-01
12	1.102509e-01
13	1.168988e-01
14	9.003912e-02
15	1.087536e-01
16	9.074093e-02
17	1.121849e-01
18	1.078674e-01
19	9.559755e-02

	var
0	9.356805e+12
1	7.536911e+12
2	7.721614e+12
3	9.570564e+12
4	6.997436e+12
5	7.094525e+12
6	9.301787e+12
7	8.439373e+12
8	8.456525e+12
9	9.177452e+12
10	9.730413e+12
11	8.647925e+12
12	8.322833e+12
13	8.451528e+12
14	8.063945e+12
15	8.488480e+12
16	7.545325e+12
17	8.761391e+12
18	8.631646e+12
19	8.748313e+12
0	1.358962e-02
1	1.488682e-02
2	1.358850e-02
3	1.522040e-02
4	5.973341e-03
5	1.951945e-02
6	6.322692e-03
7	1.289682e-02
8	1.176063e-02
9	8.569198e-03
10	8.046064e-03
11	1.469477e-02
12	6.677408e-03
13	1.165956e-02
14	9.374600e-03
15	1.667622e-02
16	1.427966e-02
17	1.134147e-02
18	1.024677e-02
19	6.676352e-03

	std
0	3.058889e+06
1	2.745344e+06
2	2.778779e+06
3	3.093633e+06
4	2.645267e+06
5	2.663555e+06
6	3.049883e+06
7	2.905060e+06
8	2.908011e+06
9	3.029431e+06
10	3.119361e+06
11	2.940735e+06
12	2.884932e+06
13	2.907151e+06
14	2.839709e+06
15	2.913500e+06
16	2.746875e+06
17	2.959965e+06
18	2.937966e+06
19	2.957755e+06
0	1.165745e-01
1	1.220116e-01
2	1.165697e-01
3	1.233710e-01
4	7.728740e-02
5	1.397120e-01
6	7.951536e-02
7	1.135642e-01
8	1.084464e-01
9	9.256996e-02
10	8.969986e-02
11	1.212220e-01
12	8.171541e-02
13	1.079794e-01
14	9.682252e-02
15	1.291364e-01
16	1.194975e-01
17	1.064963e-01
18	1.012263e-01
19	8.170895e-02